Journal of Korean Society on Water Environment (한국물환경학회지)

Korean Society on Water Environment (한국물환경학회)
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Study on the Modelling of Algal Dynamics in Lake Paldang Using Artificial Neural Networks

인공신경망을 이용한 팔당호의 조류발생 모델 연구

  • Park, Hae-Kyung (Water Environment Research Department, National Institute of Environmental Research) ;
  • Kim, Eun-Kyoung (Water Environment Research Department, National Institute of Environmental Research)
  • 박혜경 (국립환경과학원 물환경연구부) ;
  • 김은경 (국립환경과학원 물환경연구부)
  • Published : 2013.01.30
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Abstract

Artificial neural networks were used for time series modelling of algal dynamics of whole year and by season at the Paldang dam station (confluence area). The modelling was based on comprehensive weekly water quality data from 1997 to 2004 at the Paldang dam station. The results of validation of seasonal models showed that the timing and magnitude of the observed chlorophyll a concentration was predicted better, compared with the ANN model for whole year. Internal weightings of the inputs in trained neural networks were obtained by sensitivity analysis for identification of the primary driving mechanisms in the system dynamics. pH, COD, TP determined most the dynamics of chlorophyll a, although these inputs were not the real driving variable for algal growth. Short-term prediction models that perform one or two weeks ahead predictions of chlorophyll a concentration were designed for the application of Harmful Algal Alert System in Lake Paldang. Short-term-ahead ANN models showed the possibilities of application of Harmful Algal Alert System after increasing ANN model's performance.

Keywords

References

  1. Chon, T. S., Park, Y. S., Moon, K. H., and Cha, E. Y. (1996). Patternizing Communities by Using an Artificial Neural Network, Ecological Modelling, 90, pp. 69-78. https://doi.org/10.1016/0304-3800(95)00148-4
  2. French, M. and Rechknagel, F. (1994). Modelling of Alga Blooms in Freshwaters Using Artificial Neural Networks, In: Zanetti, P. (Ed), Computer Techniques in Environmental Studies V, Environmental Systems, Vol. II, Computational Mechanics Publications, Boston, pp. 87-94.
  3. Hecht-Nielsen, R. (1987). Kolmogorov's Mapping Neural Network Existence Theorem, First IEEE International Joint Conference on Neural Networks, pp. 11-14.
  4. Imai, A., Fukushima, T., and Matsushige, K. (1999). Effects of Aquatic Humic Substances on the Growth the Cyanobacterium Microcystis aeruginosa, Japanese Journal of Water Environment Science, 22, pp. 555-560. https://doi.org/10.2965/jswe.22.555
  5. Jeong, K. S., Joo, G. J., Kim, H. W., Ha, K., and Recknagel F. (2001). Prediction and Elucidation of Phytoplankton Dynamics in the Nakdong River (Korea) by Means of a Recurrent Artificial Neural Network, Ecological Modelling, 146, pp. 115-129. https://doi.org/10.1016/S0304-3800(01)00300-3
  6. Jeong, K. S., Kim, D. K., and Joo, G. J. (2006). River Phytoplankton Prediction Model by Artificial Neural Network: Model Performance and Selection of Input Variables to Predict Time-series Phytoplankton Proliferations in a Regulated River System, Ecological Informatics, 1, pp. 235-245. https://doi.org/10.1016/j.ecoinf.2006.04.001
  7. Jones, I. D., Page, T., Elliott, J. A., Thackeray, S. J., and Heathwaite, A. L. (2011). Increases in Lake Phytoplankton Biomass Caused by Future Climate-Driven Changes to Seasonal River Flow, Global Change Biology, 17, pp. 1809-1820. https://doi.org/10.1111/j.1365-2486.2010.02332.x
  8. Kong, D. S., Yoon, I. B., and Ryu, J. K. (1996). Hydrological Characteristics and Water Budget of Lake Paldang, Korean Journal of Limnology, 29(1), pp. 51-64. [Korean Literature]
  9. Lee, J. H. W., Huang, Y., Dickman, M., and Jayawardena, A. W. (2003). Neural network Modelling of Coastal Algal Blooms, Ecological Modellng, 159, pp. 179-201. https://doi.org/10.1016/S0304-3800(02)00281-8
  10. Maier, H., Dandy, G., and Burch, M. (1998). Use of Artificial Neural Networks for Modelling Cyanobacteria Anabaena spp. in the River Murray, South Australia, Ecological Modelling, 105, pp. 257-272. https://doi.org/10.1016/S0304-3800(97)00161-0
  11. Park, H. K., Byeon, M. S., Choi, M. J., and Kim, Y. J. (2008). The Effect Factors on the Growth of Phytoplankton and the Sources of Organic Matters in Downstream of South- Han River, Journal of Korean Society on Water Environment, 24(5), pp. 556-562. [Korean Literature]
  12. Park, H. K., Byeon, M. S., Shin, Y. N., and Jung, D. I. (2009). Sources and Spatial/Temporal Characteristics of Organic Carbon in Two Large Reservoirs with Contrasting Hydrologic Characteristics, Water Resources Research, 45, W11418, doi:10.1029/2009WR008043.
  13. Park, H. K. and Jheong, W. H. (2003). Long-term changes of Algal Growth in lake Paldang, Journal of Korean Society on Water Environment, 19(6), pp. 673-684. [Korean Literature]
  14. Park, H. K., Jheong, W. H., Kwon, O. S., and Ryu, J. K. (2000). Seasoanl succession of toxic cyanobacteria and microcystin concentration in Paldang Reservoir, Algae, 15(1), pp. 277-282. [Korean Literature]
  15. Park, H. K., Kim, H. B., Lee J. J., Lee, H. J., Park, J. W., Seo, J. K., Youn, S. J., and Moon, J. S. (2011). Investigation of Criterion on Harmful Algae Alert System using Correlation between Cell Numbers and Cellular Microcystins Content of Korean Toxic Cyanobacteria, Journal of Korean Society on Water Environment, 27(4), pp. 491-498. [Korean Literature]
  16. Park, H. K., Lee, Y. H., and Jung, D. I. (2004). Organic Carbon Budget druing Rainy and Dry Period in Paldang Reservoir, Korean Journal of Limnology, 37(3), pp. 272-281. [Korean Literature]
  17. Park, H. K., Lee, H. J., Kim, E. K., and Jung, D. I. (2005). Characteristics of Algal Abundance and Statistical Analysis of Environmental Factors in Lake Paldang, Journal of Korean Society on Water Environment, 21(6), pp. 584-594. [Korean Literature]
  18. Recknagel, F. (2001). Applications of Machine Learning to Ecological Modelling, Ecological Modelling, 146, pp. 303-310. https://doi.org/10.1016/S0304-3800(01)00316-7
  19. Recknagel, F., French, M., Harkonen, P., and Yabunaka, K.-I. (1997). Artificial Neural Network Approach for Modelling and Prediction of Algal Blooms, Ecological Modelling, 96, pp. 11-28. https://doi.org/10.1016/S0304-3800(96)00049-X
  20. Recknagel, F. and Wilson, H. (2000). Elucidation and Prediction of Aquatic Ecosystems by Artificial Neuronal Networks, In: Lek, S., Guegan, J. (Eds.), Artificial Neuronal Networks Application to Ecology and Evolution, Springer, Berlin, p. 262.
  21. Rogers, L. L. and Dowla, F. U. (1994). Optimization of Groundwater Remediation Using Artificial Neural Networks with Parallel Solute Transport Modeling, Water Resources Research, 30(2), pp. 457-481. https://doi.org/10.1029/93WR01494
  22. Seretaki, T. (1996). The Response of the Algal Flora to the COD in Self-regulating Systems of Wastewater Treatment Plants, Arch. Hydrobiol., 136, pp. 543-556.
  23. Wei, B., Sugiura, N., and Maekawa, T. (2001). Use of Artificial Neural Network in the Prediction of Algal Blooms, Water Research, 35(8), pp. 2022-2028. https://doi.org/10.1016/S0043-1354(00)00464-4
  24. Yeon, I., Hong, J., and Mun, H. (2011). Estimating Optimal Parameters of Artificial Neural Networks for the Daily Forecasting of the Chlorophyll-a in a Reservoir, Journal of Korean Society on Water Environment, 27(4), pp. 533-541. [Korean Literature]